1. Field of Invention
The present invention relates to a method of reducing image noise. More particularly, the present invention relates to a method of reducing image noise by adjusting the range weight value and domain weight value of a bilateral filter according to image boundary and intensity.
2. Related Art
Among the current methods of reducing image noise, some employ median filter, mean filter, or low pass filter (LPF) and so on for reducing image noise. However, the above methods achieve the purpose by evening pixels in the entire image without making a difference between a smooth area (i.e. the area where most pixels are similar) and a detailed area (i.e. the area containing a boundary) of the image. When the smooth area and the detailed area coexist in an image, the detailed area may become much fuzzier as the pixels therein are evened out together with those in the smooth area. Meanwhile, the method of reducing image noise through evening usually has to make a choice between reducing the image noise and preserving the detailed area, thus making it impossible to improve the image quality. Therefore, a method of reducing image noise by a bilateral filter was proposed in year 1998.
A bilateral filter employs two filters related to domain and range to reconstruct every pixel in an image. The filter related to domain is called domain filter, indicating that the closer a reference pixel is to a target pixel, the higher its reference value will be. Thus, the domain weight value (Wdomain) of a reference pixel closer to a target pixel is higher when the target pixel is reconstructed according to each reference pixel. The filter related to range is called range filter, indicating that the more a surrounding reference pixel is similar to a target pixel, the higher its reference value will be. Thus, the range weight value (Wrange) of a reference pixel more similar to a target pixel is higher when the target pixel is reconstructed according to each reference pixel. Both domain filter and range filter are shift-invariant Gaussian filters. As shown in FIG. 1, if the bilateral filter uses a 3×3 table 100 as a mask of reference scope, a target pixel 101 in the center of the table 100 is reconstructed according to the surrounding first reference pixel 102 to eighth reference pixel 109, and the reconstruction formula is:
      P    result    =                    ∑                              P            i                    ∈          Mask                    ⁢                        P          i                ×                  W                      range            ,            i                          ×                  W                      domain            ,            i                                      ∑                        W                      range            ,            i                          ×                  W                      domain            ,            i                              
in which i equals to 1 to 8, respectively corresponding to the first reference pixel 102 to the eighth reference pixel 109, i.e., Presult is the target pixel 101 after reconstruction; Wrange,1 is the range weight value of the first reference pixel 102 to the target pixel 101; Wdomain,1 is the domain weight value of the first reference pixel 102 to the target pixel 101, and Wrange,2, Wdomain,2, etc., can be deduced by analogy.
Under the circumstance that more and more high ISO images emerge at present, as high image noise is bound to accompany, the image noise of the high ISO images generally has to be reduced. Though the method of reducing noise by a bilateral filter, the smooth area and the detailed area can be treated separately to improve the image quality, but the effect of image quality improvement by a bilateral filter in reducing high image noise is limited.